Impact factors and publication times of original scientific research in radiology journals
Bibliographic record
Abstract
PURPOSE: The time from article submission to publication in peer-reviewed scientific journals is variable and can be prolonged, which slows the dissemination of research and can influence the academic progress of authors. This study evaluated the publication times for articles in radiology journals, in particular the relationship between turnaround times and journal impact factors (IFs). METHODS: Bibliometric data was obtained from Journal Citation Reports to conduct a comparative analysis of radiology journals against those in other disciplines of clinical medicine using highest IF, median IF, cited half-life, immediacy index, and number of journals. Journals from various radiology subcategories were further examined to assess IF trends over time. The Pearson correlation coefficient was used to identify any statistically significant relationships between IF and other variables. RESULTS: Among 28 medical disciplines, there was a significant positive correlation of 0.63 between the number of journals and the highest journal IF of a given discipline. Among 135 radiology journals categorized into 12 subcategories, there was a similar significant correlation of 0.64. For high-ranking radiology journals, the median time from submission to publication online was 22.7 weeks [IQR = 9.3] and median time from submission to publication in print was 37.9 weeks [IQR = 7.1]. The former time interval showed a positive correlation of 0.58 with journal IF at p < 0.05. CONCLUSION: There is wide variation in the time from submission to publication in radiology journals. Authors can expect a longer turnaround time when publishing in higher-impact journals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".